Methodsĭata for 637 malignant breast cancer patients admitted in the south-east region of Sweden were analyzed. This study describes the application of CCA to find important predictors for two different outcomes for breast cancer patients, loco-regional recurrence and occurrence of distant metastasis and to decrease the number of variables in the sets of predictors and outcomes without decreasing the predictive strength of the model. It is important to understand the relationship between different predictors and recurrence, including the time interval until recurrence. One essential outcome after breast cancer treatment is recurrence of the disease. In contrast, Canonical Correlation Analysis (CCA) has the ability to analyze multiple outcomes at the same time. If there is more than one outcome variable, the analysis must then be repeated, and the results combined in some arbitrary fashion. A common approach in exploring register data is to find relationships between outcomes and predictors by using multiple regression analysis (MRA).
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